Crop yield prediction method and system based on low-altitude remote sensing information from unmanned aerial vehicle

ABSTRACT

Disclosed a crop yield prediction method and system based on low-altitude remote sensing information from an unmanned aerial vehicle (UAV). Obtaining a plurality of images taken by the UAV; stitching the plurality of images to obtain a stitched image; performing spectral calibration on the stitched image to obtain the reflectivity of each pixel in the stitched image; using a threshold segmentation method to segment the stitched image, to obtain a target area for crop yield prediction; using a Pearson correlation analysis method to analyze a correlation between the reflectivity of each band and the growth status and yield of the crop to obtain feature bands; constructing yield prediction factors based on the feature bands; and determining a predicted crop yield value of the target area for crop yield prediction based on the yield prediction factors and a crop planting area of the target area for crop yield prediction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of and priority to Chinese applicationno. 202010417855.X, filed May 18, 2020, the entirety of which isincorporated by reference herein.

TECHNICAL FIELD

The present invention relates to the field of yield prediction, and inparticular, to a crop yield prediction method and system based onlow-altitude remote sensing information from an unmanned aerial vehicle(UAV).

BACKGROUND

Monitoring the planting area and yield of food crops has always beenhighly valued. Governments and scientific researchers are committed tostudying how to timely and accurately learn the information such as theyield of food crops. In addition, the timely acquisition of cropplanting information can provide a scientific basis for governments toformulate agricultural production policies, which is essential forensuring food security. In agricultural production, the timely andaccurate prediction of crop yield is also essential for betterimplementation of crop management, especially in making of inputdecisions for crop insurance, harvest planning, storage needs, cash flowbudget, nutrition, pesticides, and water.

The traditional crop yield prediction methods mainly rely on thefarmers' empirical knowledge or large-scale destructive sampling, whichhave problems of high labor intensity and low prediction accuracy.

SUMMARY

The present invention provides a crop yield prediction method and systembased on low-altitude remote sensing information from a UAV, to improvethe crop yield prediction accuracy and reduce the labor intensity.

To achieve the above purpose, the present invention provides thefollowing technical solutions:

A crop yield prediction method based on low-altitude remote sensinginformation from a UAV includes:

obtaining a plurality of images taken by a UAV, where the UAV uses amulti-spectral camera to shoot crop canopies to obtain reflectionspectrum images of a plurality of different bands;

stitching the plurality of images to obtain a stitched image, where thestitched image includes a plurality of spectral calibration plates;

performing spectral calibration on the stitched image based on acalibration coefficient of the spectral calibration plate to obtain thereflectivity of each pixel in the stitched image;

using a threshold segmentation method to segment the stitched imagebased on the reflectivity of each pixel, to obtain a target area forcrop yield prediction;

using a Pearson correlation analysis method to analyze a correlationbetween the reflectivity of each band and the growth status and yield ofthe crop to obtain feature bands, where the feature bands are aplurality of bands with the highest correlation;

constructing yield prediction factors based on the feature bands; and

determining a predicted crop yield value of the target area for cropyield prediction based on the yield prediction factors and a cropplanting area of the target area for crop yield prediction.

Optionally, the stitching the plurality of images to obtain a stitchedimage specifically includes:

obtaining a forward overlap rate and a side overlap rate of the imagestaken by the UAV, where the forward overlap rate is an overlap rate oftwo adjacent aerial images taken by the UAV on a same flight strip; andthe side overlap rate is an overlap rate of shooting ranges on twoadjacent flight strips of the UAV; and

performing image stitching on all images based on the forward overlaprate and the side overlap rate, and performing orthophoto correction toobtain the stitched image.

Optionally, the performing spectral calibration on the stitched imagebased on a calibration coefficient of the spectral calibration plate toobtain the reflectivity of each pixel in the stitched image specificallyincludes:

obtaining the reflectivity of each spectral calibration plate to obtaina calibration coefficient of each spectral calibration plate;

performing function fitting based on a coordinate point corresponding toeach spectral calibration plate to obtain a reflectivity correctionfunction, where an x-coordinate of the coordinate point corresponding tothe spectral calibration plate is an average spectral value of allpixels in the spectral calibration plate, a y-coordinate of thecoordinate point corresponding to the spectral correction plate is acalibration coefficient of the spectral calibration plate, and thereflectivity correction function is R=k*DN+b, where R is thereflectivity of a pixel, DN is a spectral value of the pixel, and k andb are coefficients in the reflectivity correction function; and

obtaining the reflectivity of each pixel in the stitched image based onthe spectral value of each pixel in the stitched image by using thereflectivity correction function.

Optionally, the constructing yield prediction factors based on thefeature bands specifically includes:

obtaining a yield prediction factor model, where the yield predictionfactor model is:

X₁ = (B₁₁ + B₁₂) − 1X₂ = (B₂₁ − B₂₂)X₃ = (B₃₁ − B₃₂)/(B₃₁ + B₃₂)X₄ = (B₄₁/B₄₂)$X_{5} = {1.5*\frac{B_{51} - B_{52}}{B_{51} + B_{52} + {0.5}}}$$X_{6} = \frac{1.16*\left( {B_{61} - B_{62}} \right)}{B_{61} + B_{62} + {{0.1}6}}$

where X₁ is a first yield prediction factor model, B₁ is a spectralvalue of a first feature band in a first yield prediction factor, andB₁₂ is a spectral value of a second feature band in the first yieldprediction factor; X₂ is a second yield prediction factor model, B₂₁ isa spectral value of a first feature band in a second yield predictionfactor, and B₂₂ is a spectral value of a second feature band in thesecond yield prediction factor; X₃ is a third yield prediction factormodel, B₃₁ is a spectral value of a first feature band in a third yieldprediction factor, and B₃₂ is a spectral value of a second feature bandin the third yield prediction factor; X₄ is a fourth yield predictionfactor model, B₄₁ is a spectral value of a first feature band in afourth yield prediction factor, and B₄₂ is a spectral value of a secondfeature band in the fourth yield prediction factor; X₅ is a fifth yieldprediction factor model, B₅₁ is a spectral value of a first feature bandin a fifth yield prediction factor, and B₅₂ is a spectral value of asecond feature band in the fifth yield prediction factor; and X₆ is asixth yield prediction factor model, B₆₁ is a spectral value of a firstfeature band in a sixth yield prediction factor, and B₆₂ is a spectralvalue of a second feature band in the sixth yield prediction factor;

determining all band combinations, where the band combination is acombination of any two of all feature bands; and

using a sensitivity analysis method to determine the yield predictionfactors, where the yield prediction factors include the first yieldprediction factor, the second yield prediction factor, the third yieldprediction factor, the fourth yield prediction factor, the fifth yieldprediction factor, and the sixth yield prediction factor; a bandcombination in the first yield prediction factor makes a firstcorrelation coefficient the highest, and the first correlationcoefficient is a correlation coefficient between the first yieldprediction factor and crop yield; a band combination in the second yieldprediction factor makes a second correlation coefficient the highest,and the second correlation coefficient is a correlation coefficientbetween the second yield prediction factor and crop yield; a bandcombination in the third yield prediction factor makes a thirdcorrelation coefficient the highest, and the third correlationcoefficient is a correlation coefficient between the third yieldprediction factor and crop yield; a band combination in the fourth yieldprediction factor makes a fourth correlation coefficient the highest,and the fourth correlation coefficient is a correlation coefficientbetween the fourth yield prediction factor and crop yield; a bandcombination in the fifth yield prediction factor makes a fifthcorrelation coefficient the highest, and the fifth correlationcoefficient is a correlation coefficient between the fifth yieldprediction factor and crop yield; and a band combination in the sixthyield prediction factor makes a sixth correlation coefficient thehighest, and the sixth correlation coefficient is a correlationcoefficient between the sixth yield prediction factor and crop yield.

Optionally, the determining a predicted crop yield value of the targetarea for crop yield prediction based on the yield prediction factors anda crop planting area of the target area for crop yield predictionspecifically includes:

obtaining a unit yield prediction model, where the unit yield predictionmodel is:M=α*X ₁ +β*X ₂ +γ*X ₃ +δ*X ₄ +ε*X ₅ +θ*X ₆

where α, β, γ, δ, ε, and θ are yield prediction coefficients, and M is apredicted value of unit yield; and

determining the predicted crop yield value of the target area for cropyield prediction based on the unit yield prediction model and the cropplanting area of the target area for crop yield prediction by using aformula F=M*N, where N is the crop planting area of the target area forcrop yield prediction, and F is the predicted crop yield value in thetarget area for crop yield prediction.

A crop yield prediction system based on low-altitude remote sensinginformation from a UAV includes:

an image obtaining module, configured to obtain a plurality of imagestaken by the UAV, where the UAV uses a multi-spectral camera to shootcrop canopies to obtain reflection spectrum images of a plurality ofdifferent bands;

a stitching module, configured to stitch the plurality of images toobtain a stitched image, where the stitched image includes a pluralityof spectral calibration plates;

a spectral calibration module, configured to perform spectralcalibration on the stitched image based on a calibration coefficient ofthe spectral calibration plate to obtain the reflectivity of each pixelin the stitched image;

an image segmentation module, configured to use a threshold segmentationmethod to segment the stitched image based on the reflectivity of eachpixel, to obtain a target area for crop yield prediction;

a correlation analysis module, configured to use a Pearson correlationanalysis method to analyze a correlation between the reflectivity ofeach band and the growth status and yield of the crop to obtain featurebands, where the feature bands are a plurality of bands with the highestcorrelation;

a yield prediction factor construction module, configured to constructyield prediction factors based on the feature bands; and

a yield prediction value determining module, configured to determine apredicted crop yield value of the target area for crop yield predictionbased on the yield prediction factors and a crop planting area of thetarget area for crop yield prediction.

Optionally, the stitching module specifically includes:

an aerial image overlap obtaining unit, configured to obtain a forwardoverlap rate and a side overlap rate of the images taken by the UAV,where the forward overlap rate is an overlap rate of two adjacent aerialimages taken by the UAV on a same flight strip; and the side overlaprate is an overlap rate of shooting ranges on two adjacent flight stripsof the UAV; and

an image stitching unit, configured to perform image stitching on allimages based on the forward overlap rate and the side overlap rate, andperform orthophoto correction to obtain the stitched image.

Optionally, the spectral calibration module specifically includes:

a calibration coefficient obtaining unit, configured to obtain thereflectivity of each spectral calibration plate, to obtain a calibrationcoefficient of each spectral calibration plate;

a reflectivity correction function fitting unit, configured to performfunction fitting based on a coordinate point corresponding to eachspectral calibration plate to obtain a reflectivity correction function,where an x-coordinate of the coordinate point corresponding to thespectral calibration plate is an average spectral value of all pixels inthe spectral calibration plate, a y-coordinate of the coordinate pointcorresponding to the spectral correction plate is a calibrationcoefficient of the spectral calibration plate, and the reflectivitycorrection function is R=k*DN+b, where R is the reflectivity of a pixel,DN is a spectral value of the pixel, and k and b are coefficients in thereflectivity correction function; and

a reflectivity determining unit, configured to obtain the reflectivityof each pixel in the stitched image based on the spectral value of eachpixel in the stitched image by using the reflectivity correctionfunction.

Optionally, the yield prediction factor construction module specificallyincludes:

a yield prediction factor model obtaining unit, configured to obtain ayield prediction factor model, where the yield prediction factor modelis:

X₁ = (B₁₁ + B₁₂) − 1 X₂ = (B₂₁ − B₂₂) X₃ = (B₃₁ − B₃₂)/(B₃₁ + B₃₂)X₄ = (B₄₁/B₄₂)$X_{5} = {1.5*\frac{B_{51} - B_{52}}{B_{51} + B_{52} + {0.5}}}$$X_{6} = \frac{1.16*\left( {B_{61} - B_{62}} \right)}{B_{61} + B_{62} + {{0.1}6}}$

where X₁ is a first yield prediction factor model, B₁₁ is a spectralvalue of a first feature band in a first yield prediction factor, andB₁₂ is a spectral value of a second feature band in the first yieldprediction factor; X₂ is a second yield prediction factor model, B₂₁ isa spectral value of a first feature band in a second yield predictionfactor, and B₂₂ is a spectral value of a second feature band in thesecond yield prediction factor; X₃ is a third yield prediction factormodel, B₃₁ is a spectral value of a first feature band in a third yieldprediction factor, and B₃₂ is a spectral value of a second feature bandin the third yield prediction factor; X₄ is a fourth yield predictionfactor model, B₄₁ is a spectral value of a first feature band in afourth yield prediction factor, and B₄₂ is a spectral value of a secondfeature band in the fourth yield prediction factor; X₅ is a fifth yieldprediction factor model, B₅₁ is a spectral value of a first feature bandin a fifth yield prediction factor, and B₅₂ is a spectral value of asecond feature band in the fifth yield prediction factor; and X₆ is asixth yield prediction factor model, B₆₁ is a spectral value of a firstfeature band in a sixth yield prediction factor, and B₆₂ is a spectralvalue of a second feature band in the sixth yield prediction factor;

a band combination determining unit, configured to determine all bandcombinations, where the band combination is a combination of any two ofall feature bands; and

a yield prediction factor determining unit, configured to use asensitivity analysis method to determine the yield prediction factors,where the yield prediction factors include the first yield predictionfactor, the second yield prediction factor, the third yield predictionfactor, the fourth yield prediction factor, the fifth yield predictionfactor, and the sixth yield prediction factor; a band combination in thefirst yield prediction factor makes a first correlation coefficient thehighest, and the first correlation coefficient is a correlationcoefficient between the first yield prediction factor and crop yield; aband combination in the second yield prediction factor makes a secondcorrelation coefficient the highest, and the second correlationcoefficient is a correlation coefficient between the second yieldprediction factor and crop yield; a band combination in the third yieldprediction factor makes a third correlation coefficient the highest, andthe third correlation coefficient is a correlation coefficient betweenthe third yield prediction factor and crop yield; a band combination inthe fourth yield prediction factor makes a fourth correlationcoefficient the highest, and the fourth correlation coefficient is acorrelation coefficient between the fourth yield prediction factor andcrop yield; a band combination in the fifth yield prediction factormakes a fifth correlation coefficient the highest, and the fifthcorrelation coefficient is a correlation coefficient between the fifthyield prediction factor and crop yield; and a band combination in thesixth yield prediction factor makes a sixth correlation coefficient thehighest, and the sixth correlation coefficient is a correlationcoefficient between the sixth yield prediction factor and crop yield.

Optionally, the yield prediction value determining module specificallyincludes:

a unit yield prediction model obtaining unit, configured to obtain aunit yield prediction model, where the unit yield prediction model is:M=α*X ₁ +β*X ₂ +γ*X ₃ +δ*X ₄ +ε*X ₅ +θ*X ₆

where α, β, γ, δ, ε, and θ are yield prediction coefficients, and M is apredicted value of unit yield; and

a crop yield prediction unit, configured to determine the predicted cropyield value of the target area for crop yield prediction based on theunit yield prediction model and the crop planting area of the targetarea for crop yield prediction by using a formula F=M*N, where N is thecrop planting area of the target area for crop yield prediction, and Fis the predicted crop yield value in the target area for crop yieldprediction.

According to specific examples provided by the present invention, thepresent invention discloses the following technical effects.

(1) The labor intensity and operating costs are low, and the operatingefficiency is high. Traditional crop yield prediction requiresmeasurement of row spacing and plant spacing, as well as destructivesampling to measure parameters such as the total grains per panicle,seed setting rate, and grain weight to finally calculate the predictedyield per mu, which is labor-intensive, costly, and time-consuming. Thepresent invention uses a UAV to collect crop canopy spectrum image data.With a flying speed of 2.5 m/s, the UAV allows fast estimation of thecrop yield in a large area without any personnel entering the paddyfield.

(2) The accuracy and stability are high. The traditional crop yieldprediction method is highly subjective and mainly depends on theprofessional knowledge of the operators. The difference in theprofessional knowledge and experience of the operators leads todifferent measurement accuracy, making it difficult to maintain thestability and credibility. The equipment of the present invention isstable, and the measurement process is fixed and streamlined, withouthuman impacts. Production verifications show that the accuracy is morethan 96% and the stability is more than 98%.

BRIEF DESCRIPTION OF DRAWINGS

To describe the technical solutions in the examples of the presentinvention or in the prior art more clearly, the following brieflydescribes the accompanying drawings required for the examples.Apparently, the accompanying drawings in the following description showmerely some examples of the present invention, and a person of ordinaryskill in the art may still derive other accompanying drawings from theseaccompanying drawings without creative efforts.

FIG. 1 is a schematic flowchart for a crop yield prediction method basedon low-altitude remote sensing information from a UAV.

FIG. 2 is a schematic structural diagram of a crop yield predictionsystem based on low-altitude remote sensing information from a UAV.

FIG. 3 is a schematic flowchart of a specific example of the presentinvention.

FIG. 4 is a schematic position diagram of calibration plates accordingto a specific example of the present invention.

FIG. 5 is an aerial image according to a specific example of the presentinvention.

FIG. 6 shows correlation coefficients of yield prediction factorscorresponding to different band combinations according to a specificexample of the present invention.

FIG. 7 is a diagram of predicted yield distribution according to aspecific example of the present invention.

DETAILED DESCRIPTION

The following clearly and completely describes the technical solutionsin the examples of the present invention with reference to accompanyingdrawings in the examples of the present invention. Apparently, thedescribed examples are merely a part rather than all of the examples ofthe present invention. All other examples obtained by a person ofordinary skill in the art based on the examples of the present inventionwithout creative efforts shall fall within the protection scope of thepresent invention.

To make the objectives, features and advantages of the present inventionmore apparent and comprehensible, the present invention is described inmore detail below with reference to the accompanying drawings andspecific implementations.

FIG. 1 is a schematic flowchart for a crop yield prediction method basedon low-altitude remote sensing information from a UAV. As shown in FIG.1 , the crop yield prediction method based on low-altitude remotesensing information from a UAV includes the following steps:

Step 100: Obtain a plurality of images taken by the UAV. The UAV uses amulti-spectral camera to shoot crop canopies to obtain reflectionspectrum images of a plurality of different bands. Before theinformation collection through UAV aerial photography, a plurality ofspectral calibration plates are placed in unobstructed areas of a targetarea for crop yield prediction for subsequent spectral correction. Someof the images taken include the spectral calibration plates. Themulti-spectral camera is arranged in a heading direction of the UAV andis set to follow yaw mode. During image information collection, aself-stabilizing gimbal of the UAV ensures that the lens direction isperpendicular to the ground. A flight control system of the UAV sends atrigger signal at fixed time intervals. The multi-spectral camera and aposition and attitude recorder take photos and collect information. Thetrigger signal frequency is not greater than 1.5 Hz. The multi-spectralcamera captures reflection spectrum information of light in differentbands of the crop canopy. The position and attitude recorder obtains GPSgeographic location information (longitude and latitude), a pitch angle,a roll angle, and a heading angle of the UAV, altitude, and illuminanceinformation, and then feeds back such data to path planning software ofthe UAV to further plan a flight path for the UAV.

Step 200: Stitch a plurality of images to obtain a stitched image.Images are stitched according to the same features of forward overlapand side overlap images. Specifically, obtain a forward overlap rate anda side overlap rate of the images taken by the UAV, where the forwardoverlap rate is an overlap rate of two adjacent aerial images taken bythe UAV on a same flight strip; and the side overlap rate is an overlaprate of shooting ranges on two adjacent flight strips of the UAV. Then,perform image stitching on all images based on the forward overlap rate,the side overlap rate, and the same features of adjacent images, andperform orthophoto correction to obtain the stitched image, where thestitched image includes all spectral calibration plates.

Step 300: Perform spectral correction on the stitched image based on acalibration coefficient of the spectral calibration plate to obtain thereflectivity of each pixel in the stitched image. The calibrationcoefficient of the spectral calibration plate refers to the reflectivityof each spectral calibration plate. The reflectivity of each spectralcalibration plate is known and can be obtained in advance as follows:Measure the reflected light intensity E₂ of each spectral calibrationplate under standard light of known light intensity E₁, then thereflectivity corresponding to each spectral calibration plate isR_(i)=E₁/E₂, which is the calibration coefficient of the spectralcalibration plate.

For each spectral calibration plate, an average spectral value of allpixels in the spectral calibration plate is taken as an x-coordinate,and the calibration coefficient is taken as a y-coordinate, to form acoordinate point of the spectral calibration plate. Taking four spectralcalibration plates for example, coordinate points corresponding to thefour spectral calibration plates can be obtained: (DN₁, R₁) (DN₂, R₂)(DN₃, R₃), and (DN₄, R₄). Linear fitting is performed based on thecoordinate points corresponding to all the spectral calibration platesto obtain a reflectivity correction function R=k*DN+b between thespectrum and the reflectivity, where R is the reflectivity of a pixel,DN is a spectral value of the pixel, and k and b are coefficients in thereflectivity correction function.

Further, the reflectivity of each pixel in the stitched image isobtained based on the spectral value of each pixel in the stitched imageby using the reflectivity correction function, to complete the spectralcalibration.

Step 400: Use a threshold segmentation method to segment the stitchedimage based on the reflectivity of each pixel, to obtain a target areafor crop yield prediction. Based on the reflectivity difference betweenthe target area for crop yield prediction and a non-target crop plantingarea, the threshold segmentation method is used to remove redundantparts such as roads and ridges on the corrected stitched image, toobtain the accurate target area for crop yield prediction.

Step 500: Use a Pearson correlation analysis method to analyze acorrelation between the reflectivity of each band and the growth statusand yield of the crop to obtain feature bands, where the feature bandsare a plurality of bands with the highest correlation.

Step 600: construct yield prediction factors based on the feature bands.The specific process is as follows.

Obtain a yield prediction factor model, where the yield predictionfactor model is:

X₁ = (B₁₁ + B₁₂) − 1 X₂ = (B₂₁ − B₂₂) X₃ = (B₃₁ − B₃₂)/(B₃₁ + B₃₂)X₄ = (B₄₁/B₄₂)$X_{5} = {1.5*\frac{B_{51} - B_{52}}{B_{51} + B_{52} + {0.5}}}$$X_{6} = \frac{1.16*\left( {B_{61} - B_{62}} \right)}{B_{61} + B_{62} + {{0.1}6}}$

where X₁ is a first yield prediction factor model, B₁₁ is a spectralvalue of a first feature band in a first yield prediction factor, andB₁₂ is a spectral value of a second feature band in the first yieldprediction factor; X₂ is a second yield prediction factor model, B₂₁ isa spectral value of a first feature band in a second yield predictionfactor, and B₂₂ is a spectral value of a second feature band in thesecond yield prediction factor; X₃ is a third yield prediction factormodel, B₃₁ is a spectral value of a first feature band in a third yieldprediction factor, and B₃₂ is a spectral value of a second feature bandin the third yield prediction factor; X₄ is a fourth yield predictionfactor model, B₄₁ is a spectral value of a first feature band in afourth yield prediction factor, and B₄₂ is a spectral value of a secondfeature band in the fourth yield prediction factor; X₅ is a fifth yieldprediction factor model, B₅₁ is a spectral value of a first feature bandin a fifth yield prediction factor, and B₅₂ is a spectral value of asecond feature band in the fifth yield prediction factor; and X₆ is asixth yield prediction factor model, B₆₁ is a spectral value of a firstfeature band in a sixth yield prediction factor, and B₆₂ is a spectralvalue of a second feature band in the sixth yield prediction factor.

Determine all band combinations, where the band combination is acombination of any two of all feature bands.

Use a sensitivity analysis method to determine the yield predictionfactors, where the yield prediction factors include the first yieldprediction factor, the second yield prediction factor, the third yieldprediction factor, the fourth yield prediction factor, the fifth yieldprediction factor, and the sixth yield prediction factor; a bandcombination in the first yield prediction factor makes a firstcorrelation coefficient the highest, and the first correlationcoefficient is a correlation coefficient between the first yieldprediction factor and crop yield; a band combination in the second yieldprediction factor makes a second correlation coefficient the highest,and the second correlation coefficient is a correlation coefficientbetween the second yield prediction factor and crop yield; a bandcombination in the third yield prediction factor makes a thirdcorrelation coefficient the highest, and the third correlationcoefficient is a correlation coefficient between the third yieldprediction factor and crop yield; a band combination in the fourth yieldprediction factor makes a fourth correlation coefficient the highest,and the fourth correlation coefficient is a correlation coefficientbetween the fourth yield prediction factor and crop yield; a bandcombination in the fifth yield prediction factor makes a fifthcorrelation coefficient the highest, and the fifth correlationcoefficient is a correlation coefficient between the fifth yieldprediction factor and crop yield; and a band combination in the sixthyield prediction factor makes a sixth correlation coefficient thehighest, and the sixth correlation coefficient is a correlationcoefficient between the sixth yield prediction factor and crop yield;the first feature bands in different yield prediction factors may be thesame or different, and the second feature bands in different yieldprediction factors may be the same or different.

Step 700: Determine a predicted crop yield value of the target area forcrop yield prediction based on the yield prediction factors and a cropplanting area of the target area for crop yield prediction. The specificprocess is as follows.

Obtain a unit yield prediction model, where the unit yield predictionmodel is:M=α*X ₁ +β*X ₂ +γ*X ₃ +δ*X ₄ +ε*X ₅ +θ*X ₆

where α, β, γ, δ, ε, and θ are yield prediction coefficients, and M is apredicted value of unit yield. The yield prediction coefficients α, β,γ, δ, ε, and θ can be obtained by substituting a known crop yield of afield and yield prediction factors corresponding to the field into theunit yield prediction model of the present invention. In specificexamples, the yield prediction coefficients can be determined in otherways.

Determine the predicted crop yield value of the target area for cropyield prediction based on the unit yield prediction model and the cropplanting area of the target area for crop yield prediction by using aformula F=M*N, where N is the crop planting area of the target area forcrop yield prediction, and F is the predicted crop yield value in thetarget area for crop yield prediction.

FIG. 2 is a schematic structural diagram of a crop yield predictionsystem based on low-altitude remote sensing information from a UAV. Asshown in FIG. 2 , the crop yield prediction system based on low-altituderemote sensing information from a UAV includes:

an image obtaining module 201, configured to obtain a plurality ofimages taken by the UAV, where the UAV uses a multi-spectral camera toshoot crop canopies to obtain reflection spectrum images of a pluralityof different bands;

a stitching module 202, configured to stitch the plurality of images toobtain a stitched image, where the stitched image includes a pluralityof spectral calibration plates;

a spectral calibration module 203, configured to perform spectralcalibration on the stitched image based on a calibration coefficient ofthe spectral calibration plate to obtain the reflectivity of each pixelin the stitched image;

an image segmentation module 204, configured to use a thresholdsegmentation method to segment the stitched image based on thereflectivity of each pixel, to obtain a target area for crop yieldprediction;

a correlation analysis module 205, configured to use a Pearsoncorrelation analysis method to analyze a correlation between thereflectivity of each band and the growth status and yield of the crop toobtain feature bands, where the feature bands are a plurality of bandswith the highest correlation;

a yield prediction factor construction module 206, configured toconstruct yield prediction factors based on the feature bands; and

a yield prediction value determining module 207, configured to determinea predicted crop yield value of the target area for crop yieldprediction based on the yield prediction factors and a crop plantingarea of the target area for crop yield prediction.

In another example, in the crop yield prediction system based onlow-altitude remote sensing information from a UAV according to thepresent invention, the stitching module 202 specifically includes:

an aerial image overlap obtaining unit, configured to obtain a forwardoverlap rate and a side overlap rate of the images taken by the UAV,where the forward overlap rate is an overlap rate of two adjacent aerialimages taken by the UAV on a same flight strip; and the side overlaprate is an overlap rate of shooting ranges on two adjacent flight stripsof the UAV; and

an image stitching unit, configured to perform image stitching on allimages based on the forward overlap rate and the side overlap rate, andperform orthophoto correction to obtain the stitched image.

In another example, in the crop yield prediction system based onlow-altitude remote sensing information from a UAV according to thepresent invention, the spectral calibration module 203 specificallyincludes:

a calibration coefficient obtaining unit, configured to obtain thereflectivity of each spectral calibration plate to obtain a calibrationcoefficient of each spectral calibration plate;

a reflectivity correction function fitting unit, configured to performfunction fitting based on a coordinate point corresponding to eachspectral calibration plate to obtain a reflectivity correction function,where an x-coordinate of the coordinate point corresponding to thespectral calibration plate is an average spectral value of all pixels inthe spectral calibration plate, a y-coordinate of the coordinate pointcorresponding to the spectral correction plate is a calibrationcoefficient of the spectral calibration plate, and the reflectivitycorrection function is R=k*DN+b, where R is the reflectivity of a pixel,DN is a spectral value of the pixel, and k and b are coefficients in thereflectivity correction function; and

a reflectivity determining unit, configured to obtain the reflectivityof each pixel in the stitched image based on the spectral value of eachpixel in the stitched image by using the reflectivity correctionfunction.

In another example, in the crop yield prediction system based onlow-altitude remote sensing information from a UAV according to thepresent invention, the yield prediction factor construction module 206specifically includes:

a yield prediction factor model obtaining unit, configured to obtain ayield prediction factor model, where the yield prediction factor modelis:

X₁ = (B₁₁ + B₁₂) − 1 X₂ = (B₂₁ − B₂₂) X₃ = (B₃₁ − B₃₂)/(B₃₁ + B₃₂)X₄ = (B₄₁/B₄₂)$X_{5} = {1.5*\frac{B_{51} - B_{52}}{B_{51} + B_{52} + {0.5}}}$$X_{6} = \frac{1.16*\left( {B_{61} - B_{62}} \right)}{B_{61} + B_{62} + {{0.1}6}}$

where X₁ is a first yield prediction factor model, B₁₁ is a spectralvalue of a first feature band in a first yield prediction factor, andB₁₂ is a spectral value of a second feature band in the first yieldprediction factor; X₂ is a second yield prediction factor model, B₂₁ isa spectral value of a first feature band in a second yield predictionfactor, and B₂₂ is a spectral value of a second feature band in thesecond yield prediction factor; X₃ is a third yield prediction factormodel, B₃₁ is a spectral value of a first feature band in a third yieldprediction factor, and B₃₂ is a spectral value of a second feature bandin the third yield prediction factor; X₄ is a fourth yield predictionfactor model, B₄₁ is a spectral value of a first feature band in afourth yield prediction factor, and B₄₂ is a spectral value of a secondfeature band in the fourth yield prediction factor; X₅ is a fifth yieldprediction factor model, B₅₁ is a spectral value of a first feature bandin a fifth yield prediction factor, and B₅₂ is a spectral value of asecond feature band in the fifth yield prediction factor; and X₆ is asixth yield prediction factor model, B₆₁ is a spectral value of a firstfeature band in a sixth yield prediction factor, and B₆₂ is a spectralvalue of a second feature band in the sixth yield prediction factor;

a band combination determining unit, configured to determine all bandcombinations, where the band combination is a combination of any two ofall feature bands; and

a yield prediction factor determining unit, configured to use asensitivity analysis method to determine the yield prediction factors,where the yield prediction factors include the first yield predictionfactor, the second yield prediction factor, the third yield predictionfactor, the fourth yield prediction factor, the fifth yield predictionfactor, and the sixth yield prediction factor; a band combination in thefirst yield prediction factor makes a first correlation coefficient thehighest, and the first correlation coefficient is a correlationcoefficient between the first yield prediction factor and crop yield; aband combination in the second yield prediction factor makes a secondcorrelation coefficient the highest, and the second correlationcoefficient is a correlation coefficient between the second yieldprediction factor and crop yield; a band combination in the third yieldprediction factor makes a third correlation coefficient the highest, andthe third correlation coefficient is a correlation coefficient betweenthe third yield prediction factor and crop yield; a band combination inthe fourth yield prediction factor makes a fourth correlationcoefficient the highest, and the fourth correlation coefficient is acorrelation coefficient between the fourth yield prediction factor andcrop yield; a band combination in the fifth yield prediction factormakes a fifth correlation coefficient the highest, and the fifthcorrelation coefficient is a correlation coefficient between the fifthyield prediction factor and crop yield; and a band combination in thesixth yield prediction factor makes a sixth correlation coefficient thehighest, and the sixth correlation coefficient is a correlationcoefficient between the sixth yield prediction factor and crop yield.

In another example, in the crop yield prediction system based onlow-altitude remote sensing information from a UAV according to thepresent invention, the yield prediction value determining module 207specifically includes:

a unit yield prediction model obtaining unit, configured to obtain aunit yield prediction model, where the unit yield prediction model is:M=α*X ₁ +β*X ₂ +γ*X ₃ +δ*X ₄ +ε*X ₅ +θ*X ₆

where α, β, γ, δ, ε, and θ are yield prediction coefficients, and M is apredicted value of unit yield; and

a crop yield prediction unit, configured to determine the predicted cropyield value of the target area for crop yield prediction based on theunit yield prediction model and the crop planting area of the targetarea for crop yield prediction by using a formula F=M*N, where N is thecrop planting area of the target area for crop yield prediction, and Fis the predicted crop yield value in the target area for crop yieldprediction.

The following further describes the solution of the present invention incombination with a specific example.

FIG. 3 is a schematic flowchart of a specific example of the presentinvention. As shown in FIG. 3 , the example includes the followingsteps:

Step 1: Place four standard spectral calibration plates in a target areafor crop yield prediction.

Step 2: At the initial stage of crop heading, use a UAV with amulti-spectral imaging system for aerial photography to collect cropmulti-spectral image information.

Step 3: Complete multi-spectral image stitching based on image features.

Step 4: Perform image distortion correction and spectral calibrationprocessing on an obtained image.

Step 5: Segment the image to obtain the target area for crop yieldprediction.

Step 6: Extract a spectral value of each band in the target area.

Step 7: Select spectral values in a band range of 611 nm to 870 nm basedon a correlation between the spectral value of each band of the cropcanopy and the growth status and yield of the crop.

Step 8: Construct yield prediction factors by using an algorithm forcalculating the spectral values of each band determined based on afeature band.

Step 9: Select a feature yield prediction factor through sensitivityanalysis.

Step 10: Substitute the yield prediction factor into a crop yieldprediction formula, to calculate the crop yield per mu in the targetarea for crop yield prediction.

Step 11: Calculate a total yield based on a crop planting area and thepredicted crop yield per mu in the target area.

FIG. 4 is a schematic position diagram of calibration plates accordingto a specific example of the present invention. As shown in FIG. 2 ,under the sunny and cloudless conditions, four spectral calibrationplates and one calibration cloth are placed near the target crop field.In this example, the SH-8-px-RS-02 eight-rotor UAV and UAV gimbaldesigned and produced by Zhejiang University are equipped with an RGBcamera and a 25-band multi-spectral camera to collect crop field imagesand spectral information. The flying height of the UAV is set to 25meters, the flying speed is 2.5 m/s, and the shooting angle is alwaysvertically downward, covering the entire crop test area.

The multi-spectral camera is arranged in a heading direction of the UAVand is set to follow yaw mode, a forward overlap rate is set to 60%, anda side overlap rate is set to 55%. During image information collection,the self-stabilizing gimbal ensures that the lens direction isperpendicular to the ground. A flight control system of the UAV sends atrigger signal at a fixed time interval of 0.91 s. The multi-spectralcamera and a position and attitude recorder take photos and collectinformation. FIG. 5 is an aerial image according to the specific exampleof the present invention.

Then, based on the same features of the forward overlap and side overlapimages, image stitching is performed by using stitching software, andimage spectrum calibration is performed by using the four calibrationplates and a calibration coefficient. The corrected multi-spectral imageis segmented based on different spectral values of the target area forcrop yield prediction and a surrounding area to remove unnecessary partssuch as roads and ridges to obtain an accurate target area for cropyield prediction. Disturbing multi-spectral information such as soil andwater in the field is removed by using a soil background removalalgorithm, to further extract a spectral value of each pixel in thetarget area for crop yield prediction, as well as the overall maximum,minimum, and average values of each band.

The feature bands are selected based on the correlation between thespectral value of each band of the crop canopy and the growth status andyield of the crop. Table 1 shows a correlation between the bands andyields.

TABLE 1 Correlation between bands and yields Band 603 nm 611 nm 624 nm632 nm 641 nm 649 nm 657 nm 666 nm 674 nm Coefficient 0.175 −0.429**−0.265* −0.399** −0.413** −0.267* −0.567** −0.488** −0.317** Band 679 nm693 nm 718 nm 732 nm 745 nm 758 nm 771 nm 784 nm 796 nm Coefficient0.230* 0.250* 0.442** 0.559** 0.558** 0.610** 0.589** 0.610** 0.646**Band 808 nm 827 nm 838 nm 849 nm 859 nm 868 nm 870 nm Coefficient0.643** 0.581** 0.605** 0.647** 0.583** 0.509** 0.534**

In this example, 24 highly correlated bands in the range of 611 nm to870 nm are selected as feature bands.

During construction of the yield prediction factors, because the 24 bandspectral values in the range of 611 nm to 870 nm can be used for a firstfeature band and a second feature band corresponding to each yieldprediction factor, each yield prediction factor X_(n) (n=1, 2, 3, 4, 5,6) has 576 (24*24) band combinations. To find a band combination withthe highest correlation coefficient between the yield prediction factorand the crop yield, sensitivity analysis (existing method) is used toselect the first feature band and the second feature band correspondingto each yield prediction factor. FIG. 6 shows correlation coefficientsof yield prediction factors corresponding to different band combinationsaccording to a specific example of the present invention. As shown inFIG. 6 , (a) shows a correlation coefficient between yield predictionfactor X₁ and the yield; (b) shows a correlation coefficient betweenyield prediction factor X₂ and the yield; (c) shows a correlationcoefficient between yield prediction factor X₃ and the yield; (d) showsa correlation coefficient between yield prediction factor X₄ and theyield; (e) shows a correlation coefficient between yield predictionfactor X₅ and the yield; and (f) shows a correlation coefficient betweenyield prediction factor X₆ and the yield. In this example, feature bandscorresponding to the yield prediction factors X₁, X₃, and X₄ are 838 nmand 784 nm; feature bands corresponding to the yield prediction factorX₂ are 745 nm and 849 nm, feature bands corresponding to the yieldprediction factor X₆ are 679 nm and 718 nm, and feature bandscorresponding to the yield prediction factor X₅ are 849 nm and 679 nm.

Finally, a predicted crop yield value of the target area for crop yieldprediction is obtained based on a unit yield prediction model containingthe yield prediction factors and the crop planting area of the targetarea for crop yield prediction. FIG. 7 shows distributed yieldprediction according to a specific example of the present invention. Theyield prediction coefficients α, β, γ, δ, ε, and θ in the unit yieldprediction model in this example can be obtained by substituting knowncrop yields of fields and corresponding yield prediction factors intothe unit yield prediction model of the present invention.

Each example of the present specification is described in a progressivemanner, each example focuses on the difference from other examples, andthe same and similar parts between the examples may refer to each other.For a system disclosed in the examples, since it corresponds to themethod disclosed in the examples, the description is relatively simple,and reference can be made to the method description.

In this invention, several examples are used for illustration of theprinciples and implementations of the present invention. The descriptionof the foregoing examples is used to help illustrate the method of thepresent invention and the core principles thereof. In addition, those ofordinary skill in the art can make various modifications in terms ofspecific implementations and scope of application in accordance with theteachings of the present invention. In conclusion, the content of thepresent specification shall not be construed as a limitation to thepresent invention.

What is claimed is:
 1. A crop yield prediction method based onlow-altitude remote sensing information from an unmanned aerial vehicle(UAV), comprising: obtaining a plurality of images taken by a UAV,wherein the UAV uses a multi-spectral camera to shoot crop canopies toobtain reflection spectrum images of a plurality of different bands;stitching the plurality of images to obtain a stitched image, whereinthe stitched image comprises a plurality of spectral calibration plates;performing spectral calibration on the stitched image based on acalibration coefficient of a spectral calibration plate to obtainreflectivity of each pixel in the stitched image; using a thresholdsegmentation method to segment the stitched image based on thereflectivity of each pixel, to obtain a target area for crop yieldprediction; using a Pearson correlation analysis method to analyze acorrelation between reflectivity of each band and a growth status andyield of the crop to obtain feature bands, wherein the feature bands area plurality of bands with the highest correlation; constructing yieldprediction factors based on the feature bands; and determining apredicted crop yield value of the target area for crop yield predictionbased on the yield prediction factors and a crop planting area of thetarget area for crop yield prediction.
 2. The crop yield predictionmethod based on low-altitude remote sensing information from a UAVaccording to claim 1, wherein the stitching the plurality of images toobtain a stitched image comprises: obtaining a forward overlap rate anda side overlap rate of the images taken by the UAV, wherein the forwardoverlap rate is an overlap rate of two adjacent aerial images taken bythe UAV on a same flight strip; and the side overlap rate is an overlaprate of shooting ranges on two adjacent flight strips of the UAV; andperforming image stitching on all images based on the forward overlaprate and the side overlap rate, and performing orthophoto correction toobtain the stitched image.
 3. The crop yield prediction method based onlow-altitude remote sensing information from a UAV according to claim 1,wherein the performing spectral calibration on the stitched image basedon a calibration coefficient of the spectral calibration plate to obtainthe reflectivity of each pixel in the stitched image comprises:obtaining the reflectivity of each spectral calibration plate to obtaina calibration coefficient of each spectral calibration plate; performingfunction fitting based on a coordinate point corresponding to eachspectral calibration plate to obtain a reflectivity correction function,wherein an x-coordinate of the coordinate point corresponding to thespectral calibration plate is an average spectral value of all pixels inthe spectral calibration plate, a y-coordinate of the coordinate pointcorresponding to the spectral correction plate is a calibrationcoefficient of the spectral calibration plate, and the reflectivitycorrection function is R=k*DN+b, wherein R is the reflectivity of apixel, DN is a spectral value of the pixel, and k and b are coefficientsin the reflectivity correction function; and obtaining the reflectivityof each pixel in the stitched image based on the spectral value of eachpixel in the stitched image by using the reflectivity correctionfunction; wherein the constructing yield prediction factors based on thefeature bands comprises: obtaining a yield prediction factor model,wherein the yield prediction factor model is: X₁ = (B₁₁ + B₁₂) − 1X₂ = (B₂₁ − B₂₂) X₃ = (B₃₁ − B₃₂)/(B₃₁ + B₃₂) X₄ = (B₄₁/B₄₂)$X_{5} = {1.5*\frac{B_{51} - B_{52}}{B_{51} + B_{52} + {0.5}}}$$X_{6} = \frac{1.16*\left( {B_{61} - B_{62}} \right)}{B_{61} + B_{62} + {{0.1}6}}$wherein X₁ is a first yield prediction factor model, B₁₁ is a spectralvalue of a first feature band in a first yield prediction factor, andB₁₂ is a spectral value of a second feature band in the first yieldprediction factor; X₂ is a second yield prediction factor model, B₂₁ isa spectral value of a first feature band in a second yield predictionfactor, and B₂₂ is a spectral value of a second feature band in thesecond yield prediction factor; X₃ is a third yield prediction factormodel, B₃₁ is a spectral value of a first feature band in a third yieldprediction factor, and B₃₂ is a spectral value of a second feature bandin the third yield prediction factor; X₄ is a fourth yield predictionfactor model, B₄₁ is a spectral value of a first feature band in afourth yield prediction factor, and B₄₂ is a spectral value of a secondfeature band in the fourth yield prediction factor; X₅ is a fifth yieldprediction factor model, B₅₁ is a spectral value of a first feature bandin a fifth yield prediction factor, and B₅₂ is a spectral value of asecond feature band in the fifth yield prediction factor; and X₆ is asixth yield prediction factor model, B₆₁ is a spectral value of a firstfeature band in a sixth yield prediction factor, and B₆₂ is a spectralvalue of a second feature band in the sixth yield prediction factor;determining all band combinations, wherein the band combination is acombination of any two of all feature bands; and using a sensitivityanalysis method to determine the yield prediction factors, wherein theyield prediction factors comprise the first yield prediction factor, thesecond yield prediction factor, the third yield prediction factor, thefourth yield prediction factor, the fifth yield prediction factor, andthe sixth yield prediction factor; a band combination in the first yieldprediction factor makes a first correlation coefficient the highest, andthe first correlation coefficient is a correlation coefficient betweenthe first yield prediction factor and crop yield; a band combination inthe second yield prediction factor makes a second correlationcoefficient the highest, and the second correlation coefficient is acorrelation coefficient between the second yield prediction factor andcrop yield; a band combination in the third yield prediction factormakes a third correlation coefficient the highest, and the thirdcorrelation coefficient is a correlation coefficient between the thirdyield prediction factor and crop yield; a band combination in the fourthyield prediction factor makes a fourth correlation coefficient thehighest, and the fourth correlation coefficient is a correlationcoefficient between the fourth yield prediction factor and crop yield; aband combination in the fifth yield prediction factor makes a fifthcorrelation coefficient the highest, and the fifth correlationcoefficient is a correlation coefficient between the fifth yieldprediction factor and crop yield; and a band combination in the sixthyield prediction factor makes a sixth correlation coefficient thehighest, and the sixth correlation coefficient is a correlationcoefficient between the sixth yield prediction factor and crop yield. 4.The crop yield prediction method based on low-altitude remote sensinginformation from a UAV according to claim 3, wherein the determining apredicted crop yield value of the target area for crop yield predictionbased on the yield prediction factors and a crop planting area of thetarget area for crop yield prediction comprises: obtaining a unit yieldprediction model, wherein the unit yield prediction model is:M=α*X ₁ +β*X ₂ +γ*X ₃ +δ*X ₄ +ε*X ₅ +θ*X ₆ wherein α, β, γ, δ, ε, and θare yield prediction coefficients, and M is a predicted value of unityield; and determining the predicted crop yield value of the target areafor crop yield prediction based on the unit yield prediction model andthe crop planting area of the target area for crop yield prediction byusing a formula F=M*N, wherein N is the crop planting area of the targetarea for crop yield prediction, and F is the predicted crop yield valuein the target area for crop yield prediction.
 5. A crop yield predictionsystem based on low-altitude remote sensing information from an unmannedaerial vehicle (UAV), comprising a processor and a memory storingprogram codes, wherein the processor performs the stored program codesfor: obtaining a plurality of images taken by the UAV, wherein the UAVuses a multi-spectral camera to shoot crop canopies to obtain reflectionspectrum images of a plurality of different bands; stitching theplurality of images to obtain a stitched image, wherein the stitchedimage comprises a plurality of spectral calibration plates; performingspectral calibration on the stitched image based on a calibrationcoefficient of a spectral calibration plate to obtain reflectivity ofeach pixel in the stitched image; using a threshold segmentation methodto segment the stitched image based on the reflectivity of each pixel,to obtain a target area for crop yield prediction; using a Pearsoncorrelation analysis method to analyze a correlation betweenreflectivity of each band and a growth status and yield of the crop toobtain feature bands, wherein the feature bands are a plurality of bandswith the highest correlation; constructing yield prediction factorsbased on the feature bands; and determining a predicted crop yield valueof the target area for crop yield prediction based on the yieldprediction factors and a crop planting area of the target area for cropyield prediction.
 6. The crop yield prediction system based onlow-altitude remote sensing information from a UAV according to claim 5,wherein the stitching the plurality of images to obtain a stitched imagecomprises: obtaining a forward overlap rate and a side overlap rate ofthe images taken by the UAV, wherein the forward overlap rate is anoverlap rate of two adjacent aerial images taken by the UAV on a sameflight strip; and the side overlap rate is an overlap rate of shootingranges on two adjacent flight strips of the UAV; and performing imagestitching on all images based on the forward overlap rate and the sideoverlap rate, and perform orthophoto correction to obtain the stitchedimage.
 7. The crop yield prediction system based on low-altitude remotesensing information from a UAV according to claim 5, wherein theperforming spectral calibration on the stitched image based on acalibration coefficient of the spectral calibration plate to obtain thereflectivity of each pixel in the stitched image comprises: obtainingthe reflectivity of each spectral calibration plate, to obtain acalibration coefficient of each spectral calibration plate; performingfunction fitting based on a coordinate point corresponding to eachspectral calibration plate to obtain a reflectivity correction function,wherein an x-coordinate of the coordinate point corresponding to thespectral calibration plate is an average spectral value of all pixels inthe spectral calibration plate, a y-coordinate of the coordinate pointcorresponding to the spectral correction plate is a calibrationcoefficient of the spectral calibration plate, and the reflectivitycorrection function is R=k*DN+b, wherein R is the reflectivity of apixel, DN is a spectral value of the pixel, and k and b are coefficientsin the reflectivity correction function; and obtaining the reflectivityof each pixel in the stitched image based on the spectral value of eachpixel in the stitched image by using the reflectivity correctionfunction.
 8. The crop yield prediction system based on low-altituderemote sensing information from a UAV according to claim 5, wherein theconstructing yield prediction factors based on the feature bandscomprises: obtaining a yield prediction factor model, wherein the yieldprediction factor model is:X₁ = (B₁₁ + B₁₂) − 1X₂ = (B₂₁ − B₂₂)X₃ = (B₃₁ − B₃₂)/(B₃₁ + B₃₂)X₄ = (B₄₁/B₄₂)$X_{5} = {1.5*\frac{B_{51} - B_{52}}{B_{51} + B_{52} + {0.5}}}$$X_{6} = \frac{1.16*\left( {B_{61} - B_{62}} \right)}{B_{61} + B_{62} + {{0.1}6}}$wherein X₁ is a first yield prediction factor model, B₁₁ is a spectralvalue of a first feature band in a first yield prediction factor, andB₁₂ is a spectral value of a second feature band in the first yieldprediction factor; X₂ is a second yield prediction factor model, B₂₁ isa spectral value of a first feature band in a second yield predictionfactor, and B₂₂ is a spectral value of a second feature band in thesecond yield prediction factor; X₃ is a third yield prediction factormodel, B₃₁ is a spectral value of a first feature band in a third yieldprediction factor, and B₃₂ is a spectral value of a second feature bandin the third yield prediction factor; X₄ is a fourth yield predictionfactor model, B₄₁ is a spectral value of a first feature band in afourth yield prediction factor, and B₄₂ is a spectral value of a secondfeature band in the fourth yield prediction factor; X₅ is a fifth yieldprediction factor model, B₅₁ is a spectral value of a first feature bandin a fifth yield prediction factor, and B₅₂ is a spectral value of asecond feature band in the fifth yield prediction factor; and X₆ is asixth yield prediction factor model, B₆₁ is a spectral value of a firstfeature band in a sixth yield prediction factor, and B₆₂ is a spectralvalue of a second feature band in the sixth yield prediction factor;determining all band combinations, wherein the band combination is acombination of any two of all feature bands; and using a sensitivityanalysis method to determine the yield prediction factors, wherein theyield prediction factors comprise the first yield prediction factor, thesecond yield prediction factor, the third yield prediction factor, thefourth yield prediction factor, the fifth yield prediction factor, andthe sixth yield prediction factor; a band combination in the first yieldprediction factor makes a first correlation coefficient the highest, andthe first correlation coefficient is a correlation coefficient betweenthe first yield prediction factor and crop yield; a band combination inthe second yield prediction factor makes a second correlationcoefficient the highest, and the second correlation coefficient is acorrelation coefficient between the second yield prediction factor andcrop yield; a band combination in the third yield prediction factormakes a third correlation coefficient the highest, and the thirdcorrelation coefficient is a correlation coefficient between the thirdyield prediction factor and crop yield; a band combination in the fourthyield prediction factor makes a fourth correlation coefficient thehighest, and the fourth correlation coefficient is a correlationcoefficient between the fourth yield prediction factor and crop yield; aband combination in the fifth yield prediction factor makes a fifthcorrelation coefficient the highest, and the fifth correlationcoefficient is a correlation coefficient between the fifth yieldprediction factor and crop yield; and a band combination in the sixthyield prediction factor makes a sixth correlation coefficient thehighest, and the sixth correlation coefficient is a correlationcoefficient between the sixth yield prediction factor and crop yield. 9.The crop yield prediction system based on low-altitude remote sensinginformation from a UAV according to claim 8, wherein the determining apredicted crop yield value of the target area for crop yield predictionbased on the yield prediction factors and a crop planting area of thetarget area for crop yield prediction comprises: obtaining a unit yieldprediction model, wherein the unit yield prediction model is:M=α*X ₁ +β*X ₂ +γ*X ₃ +δ*X ₄ +ε*X ₅ +θ*X ₆ wherein α, β, γ, δ, ε, and θare yield prediction coefficients, and M is a predicted value of unityield; and determining the predicted crop yield value of the target areafor crop yield prediction based on the unit yield prediction model andthe crop planting area of the target area for crop yield prediction byusing a formula F=M*N, wherein N is the crop planting area of the targetarea for crop yield prediction, and F is the predicted crop yield valuein the target area for crop yield prediction.